Development and validation of an advanced data analytics model to support strategic point-of-care testing utilization decisions in the emergency department [Dataset]

This study was carried out to address potential uncertainties about how point-of-care testing (POCT) improves patients’ outcomes in emergency department (ED). The main aim was to develop and validate a model based on advanced data analytics to evaluate POCT’s impact in patients’ outcomes and ED pati...

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Authors: León-Justel, Antonio, Jiménez Barragán, M., Navarro Bustos, Carmen, Martín Pérez, Salomón, Garrido Castilla, José M., Morales Barroso, Isabel M., Oltra Hostalet, Fernando, Fernández Gallardo, María F., Diaz Luque, Ana, Eugenio Pizarro, Antonia, Luque Cid, Antonio, Sánchez-Mora, Catalina
Format: conjunto de datos
Status:Published version
Publication Date:2025
Country:España
Institution:Consejo Superior de Investigaciones Científicas (CSIC)
Repository:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/401736
Online Access:http://hdl.handle.net/10261/401736
https://digital.csic.es/handle/10261/401734
Access Level:Open access
Keyword:C
C6
C67
C69
Overcrowding
Advanced data analytics model
Emergency department
Point-of-care
Simulation
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spelling Development and validation of an advanced data analytics model to support strategic point-of-care testing utilization decisions in the emergency department [Dataset]León-Justel, AntonioJiménez Barragán, M.Navarro Bustos, CarmenMartín Pérez, SalomónGarrido Castilla, José M.Morales Barroso, Isabel M.Oltra Hostalet, FernandoFernández Gallardo, María F.Diaz Luque, AnaEugenio Pizarro, AntoniaLuque Cid, AntonioSánchez-Mora, CatalinaCC6C67C69OvercrowdingAdvanced data analytics modelEmergency departmentPoint-of-careSimulationThis study was carried out to address potential uncertainties about how point-of-care testing (POCT) improves patients’ outcomes in emergency department (ED). The main aim was to develop and validate a model based on advanced data analytics to evaluate POCT’s impact in patients’ outcomes and ED patients’ flow. We built a discrete event model simulation (DEMS) to represent workflow of a Spanish ED. Historical data from ED, published evidence and expert estimates were used to support the model. Different scenarios of progressive utilization of POCT in patients’ care triaged as Emergency Severity Index (ESI) level 3 were compared to standard-of-care (SoC) in terms of time-to-first medical intervention (TFMI), time-to-disposition decision (TDD), total length of stay (LoS) and patient workflow. In POCT maximum utilization scenario (60% of ESI-3 patients), time savings reached 27.44, 14.58 and 13.96 min of TFMI, 55.77, 13.64 and 13.97 min of TDD and 89.60, 18.55 and 13.98 min of LoS (ESI-3, 4 and 5 patients, respectively). Statistically significant reductions were found for all time outcomes in every POCT scenario for ESI-3, 4 and 5 patients. Internal validation didn’t show differences between model results and real data. Simplifications were made due to theoretical nature of computer-simulation models. Some input data and assumptions regarding individual process times were derived from interviews. Theoretical distributions were assumed; other activities outside the ED were considered as a disruption to the system; finally, findings reflect experience of a single ED. Advanced data analytics has become a useful tool in analyzing lots of processes. Our study showed that advanced data analytics has become an exceptional tool in clinical laboratories and exemplifies how POCT incorporation in ED for care of ESI-3 patients reduces physicians’ workload and waiting times of ESI-3, 4 and 5 patients, thus optimizing the patients’ medical journey.This work was supported by Roche Diagnostics. Roche Diagnostics played no role in the study design; in the collection, analysis, and interpretation of data; in the final writing of the report; or in the decision to submit the report for publication.Peer reviewedFigshareRocheConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202520252025info:eu-repo/semantics/datasethttp://purl.org/coar/resource_type/c_ddb1Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/mswordhttp://hdl.handle.net/10261/401736https://digital.csic.es/handle/10261/401734reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)InglésLeón-Justel, Antonio; Jiménez Barragán, M.; Navarro Bustos, Carmen; Martín Pérez, Salomón; Garrido Castilla, José M.; Morales Barroso, Isabel M.; Oltra Hostalet, Fernando; Fernández Gallardo, María F.; Diaz Luque, Ana; Eugenio Pizarro, Antonia; Luque Cid, Antonio; Sánchez-Mora, Catalina. Development and validation of an advanced data analytics model to support strategic point-of-care testing utilization decisions in the emergency department. https://doi.org/10.1080/13696998.2025.2508659. http://hdl.handle.net/10261/401734https://doi.org/10.6084/m9.figshare.29168421.v2Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/4017362026-05-22T06:33:51Z
dc.title.none.fl_str_mv Development and validation of an advanced data analytics model to support strategic point-of-care testing utilization decisions in the emergency department [Dataset]
title Development and validation of an advanced data analytics model to support strategic point-of-care testing utilization decisions in the emergency department [Dataset]
spellingShingle Development and validation of an advanced data analytics model to support strategic point-of-care testing utilization decisions in the emergency department [Dataset]
León-Justel, Antonio
C
C6
C67
C69
Overcrowding
Advanced data analytics model
Emergency department
Point-of-care
Simulation
title_short Development and validation of an advanced data analytics model to support strategic point-of-care testing utilization decisions in the emergency department [Dataset]
title_full Development and validation of an advanced data analytics model to support strategic point-of-care testing utilization decisions in the emergency department [Dataset]
title_fullStr Development and validation of an advanced data analytics model to support strategic point-of-care testing utilization decisions in the emergency department [Dataset]
title_full_unstemmed Development and validation of an advanced data analytics model to support strategic point-of-care testing utilization decisions in the emergency department [Dataset]
title_sort Development and validation of an advanced data analytics model to support strategic point-of-care testing utilization decisions in the emergency department [Dataset]
dc.creator.none.fl_str_mv León-Justel, Antonio
Jiménez Barragán, M.
Navarro Bustos, Carmen
Martín Pérez, Salomón
Garrido Castilla, José M.
Morales Barroso, Isabel M.
Oltra Hostalet, Fernando
Fernández Gallardo, María F.
Diaz Luque, Ana
Eugenio Pizarro, Antonia
Luque Cid, Antonio
Sánchez-Mora, Catalina
author León-Justel, Antonio
author_facet León-Justel, Antonio
Jiménez Barragán, M.
Navarro Bustos, Carmen
Martín Pérez, Salomón
Garrido Castilla, José M.
Morales Barroso, Isabel M.
Oltra Hostalet, Fernando
Fernández Gallardo, María F.
Diaz Luque, Ana
Eugenio Pizarro, Antonia
Luque Cid, Antonio
Sánchez-Mora, Catalina
author_role author
author2 Jiménez Barragán, M.
Navarro Bustos, Carmen
Martín Pérez, Salomón
Garrido Castilla, José M.
Morales Barroso, Isabel M.
Oltra Hostalet, Fernando
Fernández Gallardo, María F.
Diaz Luque, Ana
Eugenio Pizarro, Antonia
Luque Cid, Antonio
Sánchez-Mora, Catalina
author2_role author
author
author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Roche
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv C
C6
C67
C69
Overcrowding
Advanced data analytics model
Emergency department
Point-of-care
Simulation
topic C
C6
C67
C69
Overcrowding
Advanced data analytics model
Emergency department
Point-of-care
Simulation
description This study was carried out to address potential uncertainties about how point-of-care testing (POCT) improves patients’ outcomes in emergency department (ED). The main aim was to develop and validate a model based on advanced data analytics to evaluate POCT’s impact in patients’ outcomes and ED patients’ flow. We built a discrete event model simulation (DEMS) to represent workflow of a Spanish ED. Historical data from ED, published evidence and expert estimates were used to support the model. Different scenarios of progressive utilization of POCT in patients’ care triaged as Emergency Severity Index (ESI) level 3 were compared to standard-of-care (SoC) in terms of time-to-first medical intervention (TFMI), time-to-disposition decision (TDD), total length of stay (LoS) and patient workflow. In POCT maximum utilization scenario (60% of ESI-3 patients), time savings reached 27.44, 14.58 and 13.96 min of TFMI, 55.77, 13.64 and 13.97 min of TDD and 89.60, 18.55 and 13.98 min of LoS (ESI-3, 4 and 5 patients, respectively). Statistically significant reductions were found for all time outcomes in every POCT scenario for ESI-3, 4 and 5 patients. Internal validation didn’t show differences between model results and real data. Simplifications were made due to theoretical nature of computer-simulation models. Some input data and assumptions regarding individual process times were derived from interviews. Theoretical distributions were assumed; other activities outside the ED were considered as a disruption to the system; finally, findings reflect experience of a single ED. Advanced data analytics has become a useful tool in analyzing lots of processes. Our study showed that advanced data analytics has become an exceptional tool in clinical laboratories and exemplifies how POCT incorporation in ED for care of ESI-3 patients reduces physicians’ workload and waiting times of ESI-3, 4 and 5 patients, thus optimizing the patients’ medical journey.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/dataset
http://purl.org/coar/resource_type/c_ddb1
Publisher's version
info:eu-repo/semantics/publishedVersion
format dataset
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/401736
https://digital.csic.es/handle/10261/401734
url http://hdl.handle.net/10261/401736
https://digital.csic.es/handle/10261/401734
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv León-Justel, Antonio; Jiménez Barragán, M.; Navarro Bustos, Carmen; Martín Pérez, Salomón; Garrido Castilla, José M.; Morales Barroso, Isabel M.; Oltra Hostalet, Fernando; Fernández Gallardo, María F.; Diaz Luque, Ana; Eugenio Pizarro, Antonia; Luque Cid, Antonio; Sánchez-Mora, Catalina. Development and validation of an advanced data analytics model to support strategic point-of-care testing utilization decisions in the emergency department. https://doi.org/10.1080/13696998.2025.2508659. http://hdl.handle.net/10261/401734
https://doi.org/10.6084/m9.figshare.29168421.v2
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/msword
dc.publisher.none.fl_str_mv Figshare
publisher.none.fl_str_mv Figshare
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869406932178042880
score 15.812429

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